Computer engineering - Ingegneria informatica

The Computer Engineering group of DIEI mainly works on the following areas:

Information Visualization. Here, the main goal is the creation of tools that allow politicians, scientists, analysts, and decision-makers to visually analyze data on a massive scale and leverage them with scientific knowledge. These tools mainly focus on the analysis of networked datasets, which characterize most of the information handled in application domains. In 2009 the group founded an academic spin-off to facilitate and boost the technology transfer of its research into real-world scenarios.

Graph Drawing. Algorithms for the automatic representation of graphs and networks are a basic ingredient for the design of visual analytics tools. The group works actively in this field; two of its members participate in the steering committee of the International Symposium on Graph Drawing, the main annual event in the area, and since 2006 they organize an international workshop to promote research in this context. Both theoretical and practical aspects of graph drawing are addressed by the Computer Engineering group at DIEI.

Computational Geometry. Many graph drawing problems have a strong correlation with problems arising in computational geometry. Geometric graphs and proximity are classical topics studied in the group. In 2012, the group coordinated the program and the organizing committees of the 28th European Workshop on Computational Geometry.

Algorithm Engineering. Designing efficient algorithms and data structures is one of the crucial activities of automatic graph drawing and information visualization research. In this direction, the group has developed several software libraries and systems, some of them in cooperation with other national and international institutes, such as the University Roma Tre, the University of Ljubljana, and the University of Passau. The expertise of the group in algorithm design has been one of the key-factor in several national and European projects.

Information Retrieval and Data Mining. The use of visual analytics to support information retrieval and data mining has received an increasing attention in the last decade. A visual search clustering engine and an interactive environment for financial crime detection are among the most recent and successful systems developed by the group to this aim. Some members of the group wrote a chapter titled "Graph Visualization and Data Mining", in the book "Mining Graph Data - Ed. D. Cook and L. Holder, 2007".

AMANDA will investigate algorithmics for massive data sets. On one hand the project will study emerging and realistic computational models and general algorithm design techniques; on the other hand it will focus on algorithmic issues specific for networked data sets. Pursuing these objectives raises hard research challenges, since the size of the data as well as their networked and evolving nature require a quantum leap in algorithmic design and engineering. These challenges are addressed in two workparts (WPs), each combining theoretical analysis with extensive experimental validation:

WP1 Massive Data Sets - focused on a number of methodological issues that arise when processing very large datasets and on the design of novel algorithmic solutions for specific data-intensive applications.

Other than the University of Perugia, the AMANDA consortium includes the Third University of Rome (general coordinator), the University of Rome "La Sapienza", the University of Rome "Tor Vergata", the University of Pisa, and the University of Padova. The goal of AMANDA is to strengthen the world leading position of Italian algorithmic research and the European excellence in science in general. Some of AMANDA's expected results are likely to be exploited by industries, thus providing them support in the big data challenge, while others have a foreseeable social impact.

Designing reliable networks is a fundamental issue in many application domains, such as transportation, computer networking and power supply. The reliability of a network is often described by its robustness under errors or attacks. Most “naturally evolved” networks have been shown to be scale-free. Furthermore, it has been shown that scale-free networks are typically robust to random failures but vulnerable to targeted attacks, opposite to random networks.

The presented research is the result of a collaboration among our research group, the Brain Research Center of the National Tsing Hua University of Taiwan and the National Center for High-performance Computing of Taiwan. Recently, the Brain Research Center of the National Tsing Hua University of Taiwan published a 3D image database for single neurons, called FlyCircuit (www.flycircuit.tw). This data source has been built from the brain of Drosophila Melanogaster and it contains more than ten thousand neurons. Single neuron images were acquired and reconstructed into a standardized brain space so as to build a 3D connection atlas with physiological significance. Furthermore, the connectivity matrix raising from this neural network has been constructed. The main goal of our collaboration was to analyze this reconstructed neural network, exploiting our different expertise in biology, bioinformatics, information visualization and algorithm engineering. Indeed, this research required an interdisciplinary collaboration involving systematic image collection, coordinates unification, algorithms for graph analysis and visualization.

We demonstrate the resiliency of this reconstructed neural network under various errors and attacks. Most importantly, our results showed that the robustness of the network improved throughout the network’s development. We ran several experiments to measure the network's robustness and we adopted a visualization algorithm for clustered networks to compare the network structure before and during an attack. The information provided by the drawings of the network gave us a fundamental support in designing the experiments and analyzing the results.

Our findings can provide new clues for successful network construction and shed new light on the generation process of this neural network. Being able to understand and replicate the conditions behind such a robust structure would have a great impact in designing reliable networks in different application domains.

Crossings are considered one of the major factors that reduce the readability of the drawing of a graph. This is not only suggested by intuition but also confirmed by cognitive experiments showing that human performances degrade in the presence of edge crossings. For this reasons the study of planar drawings is a classical subject of investigation in the Graph Drawing field. Unfortunately, the graphs arising from real-world applications are often non-planar and crossings are therefore unavoidable.

Financial crimes represent a major problem of many governments and are often related to organized crimes like terrorism and narcotics trafficking. Money laundering and frauds are among the most common types of financial crimes. They are based on relevant volumes of financial transactions to conceal the identity, the source, or the destination of illegally gained money.